SMILE is now available for Visual Studio 2017.
The Program Committee of the 9th Probabilistic Graphical Models (PGM-2018) conference announced the winners of the BayesFusion Best Student Paper Award during the PGM-2018 conference banquet in Prague, Czech Republic, on September 13. The winners are:
BayesFusion Best Student Paper Award given jointly to:
Irene Córdoba, Department of Artificial Intelligence Universidad Politécnica de Madrid, Spain, for the paper entitled A Partial Orthogonalization Method for Simulating Covariance and Concentration Graph Matrices, co-authored with Gherardo Varando, Concha Bielza and Pedro Larrañaga
Kari Rantanen, HIIT, Department of Computer Science, University of Helsinki, Finland, for the paper entitled Learning Optimal Causal Graphs with Exact Search, co-authored with Antti Hyttinen and Matti Järvisalo
Gherardo Varando, Department of Artificial Intelligence Universidad Politécnica de Madrid, Spain, and Department of Mathematical Sciences, University of Copenhagen, Denmark, for being a student co-author of the paper that won the BayesFusion Best Student Paper Award, entitled A Partial Orthogonalization Method for Simulating Covariance and Concentration Graph Matrices, with Irene Córdoba, Concha Bielza and Pedro Larrañaga
Janne Leppä-aho, University of Helsinki, Department of Computer Science / HIIT, Finland, for the paper entitled Learning Non-parametric Markov Networks with Mutual Information, co-authored with Santeri Räisänen, Xiao Yang and Teemu Roos
We have added iOS to the list of platforms for which SMILE is compiled. The library is available for download.
SMILE licenses generated by our licensing server include the license for use with R/rJava (in addition to C++, Python, Java, C# and VB.NET).
BayesFusion, LLC, has offered a $1,000 cash award for the best student paper in the 9th Probabilistic Graphical Models (PGM-2018) conference, which will take place in Prague, Czech Republic, September 11-14.
BayesFusion releases an interactive model repository that allows users to open models and work with them using any web browser. The user can set evidence (or a decision in influence diagrams) and observe the impact of this observation on the rest of the model. Models in the repository are divided into small Bayesian networks, large Bayesian networks, hybrid Bayesian networks, and influence diagrams. Each of the repository models can be downloaded in order to be used locally through GeNIe.
BayesFusion has released PySMILE, a SMILE wrapper for direct use from Python. We supported Python in the past but only through jSMILE, SMILE’s Java wrapper. We strive to ensure feature symmetry between the wrappers — features available in one wrapper are generally available in other wrappers and are all described in one wrapper manual.
SMILE 1.2.1 has been released (including jSMILE and SMILE.NET wrappers). This version of SMILE offers performance improvements and bug fixes.
SMILE 1.2.0 has been released (including jSMILE and SMILE.NET wrappers). This version of SMILE includes hybrid modeling functionality, which is available in GeNIe 2.2 since August 2017.
BayesFusion, LLC, has released GeNIe 2.2 with hybrid Bayesian networks capability. Hybrid Bayesian networks allow for both discrete and continuous nodes. GeNIe 2.2 allows for full modeling freedom with no restrictions on the functional form of the interactions between variables and on the probability distributions involved in these interactions.